A Bayesian network based monitoring system for sow management

Techniques for detecting deviation from expected production level has long been known and applied outside agricultural production Even though the need for mon itoring within agriculture seem obvious only few examples exists With recent methodological improvements this may be changed As an example the present pa per describes a prototype of a system for monitoring pregnancy rate in sow herds The system is based on the Bayesian network methodology This approach leads to a monitoring systems that utilises information from matings heat detections preg nancy tests and farrowings to present updated estimates of pregnancy rates in the herd as well as probability of a change point in the level In addition the system can be used to study the e ect of number of matings on utilisation of farrowing de partment Finally when information from heat detections and pregnancy rates are included the e ect of cullings on production level can be studied The underlying model is described in detail and a simulated scenario is used for illustrating the potential of the system